Nasal airway obstruction (NAO) is a common health condition that is treated by many specialties of medicine and often needs surgical correction. The complexity of the nasal airway lends itself perfectly to the creation of a computational tool to aid clinicians in the diagnosis and treatment of NAO. In the context of surgical failure rates for treatment of NAO reported as high as 50%, none of the existing objective measures of nasal airflow patency has been shown to consistently correlate with patient symptomatology or to be an accurate predictor of successful surgical intervention. The long-term goal of this study is to develop a tool that would be universally accessible to clinicians and be accurately predictive of patient's symptoms. Even more exciting would be a tool that would aid surgeons in designing specific surgical techniques or interventions that would maximize the potential for successful outcomes. With the availability of powerful bioengineering computer-aided design software, anatomically- accurate, three dimensional computational models can be generated from patient-specific digital data captured by computed tomography (CT) scans. Computational fluid dynamics (CFD) techniques allow for the merger of anatomy with physiology by creating a virtual model of the nasal cavity with computed measures of airflow, heat transfer, and air humidification. The potential for improvements in patient outcome when CFD modeling tools are used in nasal surgical planning is enormous, particularly for previously challenging cases. In addition, unnecessary surgical procedures may potentially be reduced by allowing the physician to better select surgically treatable patients and to target specific areas of concern within the nasal valve region without guessing which of the procedures may be most beneficial to the patient. Furthermore, the computed nasal geometry can be virtually modified in a manner reflecting surgical techniques and new patterns of airflow and heat and water vapor transport can be predicted that could effectively estimate surgical outcomes - i.e virtual surgery. This study proposes to evaluate the association between this novel bioengineering tool (CFD) with patient-reported subjective measures of nasal obstruction. Furthermore, because CFD modeling allows the nasal geometry to be virtually modified in a manner reflecting surgical techniques, the findings of this study would lay the groundwork for future pre-surgical virtual surgery and predictive modeling for post-surgical outcomes with the ultimate long-term goal of improved surgical outcomes for patients with nasal airway obstruction. PUBLIC HEALTH RELEVANCE: Nasal airway obstruction (NAO) is a common health condition that crosses many specialties of medicine and affects all age groups. NAO has been shown to impact mood, energy, recreation, sleep and overall quality of life. It is estimated that annually $5 billion is spent on medications to treat NAO and an additional $60 million is spent on surgical therapy. An anatomic basis for NAO is common; it has been reported that up to 25% of the population suffers from nasal obstruction due to anatomic deformities unrelated to allergic reasons. However, the surgical correction of nasal anatomic deformities has not always been successful in improving patient's symptoms of NAO, with reported surgical failure rates as high as 50%. Computational fluid dynamics (CFD) techniques is a novel state-of-the art technology that allows for the merger of nasal anatomy with physiology. The potential for improvements in patient outcome when CFD modeling tools are used in nasal surgical planning is enormous, particularly for previously challenging cases. In addition, unnecessary surgical procedures may potentially be reduced by allowing the physician to better select surgically treatable patients and to target specific areas of concern within the nasal valve region without guessing which of the procedures may be most beneficial to the patient. In addition, the futuristic scenario of a physician using electronic medical records to download CT data into a user-friendly, simplified CFD software package to rapidly create a CFD model for each patient is not too hard to conceive. The physician can then make changes to the model with immediate computations of changes of nasal airway resistance, airflow distributions, and heat and humidity alterations. The patient would then be counseled on the appropriate surgical plan and the physician can use the virtual surgery model to help plan his/her surgical approach. Extending beyond the individual patient level, this modeling tool can serve as a powerful educational tool for physicians- in-training and paramedical personnel. Furthermore, with the universality of CT scans and the ability to post-process the raw data, the potential for telemedicine consulting for difficult nasal airway cases would also become more appealing and fruitful.